Millions of users come to online peer counseling platforms to seek support on diverse topics ranging from relationship stress to anxiety. However, studies show that online peer support groups are not always as effective as expected largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most of them often do not have systematic ways to receive guidelines or supervision. In this work, we introduce CARE: an interactive AI-based tool to empower peer counselors through automatic suggestion generation. During the practical training stage, CARE helps diagnose which specific counseling strategies are most suitable in the given context and provides tailored example responses as suggestions. Counselors can choose to select, modify, or ignore any suggestion before replying to the support seeker. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data together with advanced natural language generation techniques to achieve these functionalities. We demonstrate the efficacy of CARE by performing both quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews. We also find that CARE especially helps novice counselors respond better in challenging situations.

翻译：数百万用户涌向在线朋辈咨询平台，寻求从关系压力到焦虑等各种话题的支持。然而，研究表明，由于用户对无帮助咨询师的负面体验，在线朋辈支持群体的效果往往不如预期。朋辈咨询师是在线朋辈咨询平台成功的关键，但大多数咨询师缺乏获取指导或监督的系统性途径。在本工作中，我们介绍CARE：一款基于AI的交互式工具，通过自动生成建议来赋能朋辈咨询师。在实操训练阶段，CARE帮助诊断在特定情境中最适合的咨询策略，并提供量身定制的示例回复作为建议。咨询师在回复寻求支持者前，可以选择采纳、修改或忽略任何建议。基于动机访谈框架，CARE利用大规模咨询对话数据，结合先进的自然语言生成技术来实现这些功能。我们通过模拟聊天和半结构化访谈进行定量评估和定性用户研究，证明了CARE的有效性。我们还发现，CARE尤其能帮助新手咨询师在挑战性情境中做出更优回应。